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Full-Text Articles in Engineering
Application Of A Convolutional Neural Networks For Images Recognition, Avazjon Rakhimovich Marakhimov, Lyudmila Petrovna Varlamova
Application Of A Convolutional Neural Networks For Images Recognition, Avazjon Rakhimovich Marakhimov, Lyudmila Petrovna Varlamova
Chemical Technology, Control and Management
In the problems of image recognition, various approaches used when the image is noisy and there is a small sample of observations. The paper discusses nonparametric recognition methods and methods based on deep neural networks. This neural network allows you to collapse images, to perform downsampling as many times as necessary. Moreover, the image recognition speed is quite high, and the data dimension is reduced by using convolutional layers. One of the most important elements of the application of convolutional neural networks is training. The article gives the results of work on the application of convolutional neural networks. The work …
Hybrid Machine Learning Architecture For Automated Detection And Grading Of Retinal Images For Diabetic Retinopathy, Barath Narayanan, Barath Narayanan, Russell C. Hardie, Manawaduge Supun De Silva, Nathaniel K. Kueterman
Hybrid Machine Learning Architecture For Automated Detection And Grading Of Retinal Images For Diabetic Retinopathy, Barath Narayanan, Barath Narayanan, Russell C. Hardie, Manawaduge Supun De Silva, Nathaniel K. Kueterman
Electrical and Computer Engineering Faculty Publications
Purpose: Diabetic retinopathy is the leading cause of blindness, affecting over 93 million people. An automated clinical retinal screening process would be highly beneficial and provide a valuable second opinion for doctors worldwide. A computer-aided system to detect and grade the retinal images would enhance the workflow of endocrinologists. Approach: For this research, we make use of a publicly available dataset comprised of 3662 images. We present a hybrid machine learning architecture to detect and grade the level of diabetic retinopathy (DR) severity. We also present and compare simple transfer learning-based approaches using established networks such as AlexNet, VGG16, ResNet, …
Medical Image Segmentation With Deep Learning, Chuanbo Wang
Medical Image Segmentation With Deep Learning, Chuanbo Wang
Theses and Dissertations
Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images is time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images have been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and …
Learning In The Machine: To Share Or Not To Share?, Jordan Ott, Erik Linstead, Nicholas Lahaye, Pierre Baldi
Learning In The Machine: To Share Or Not To Share?, Jordan Ott, Erik Linstead, Nicholas Lahaye, Pierre Baldi
Engineering Faculty Articles and Research
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization …
Improved Ground-Based Monocular Visual Odometry Estimation Using Inertially-Aided Convolutional Neural Networks, Josiah D. Watson
Improved Ground-Based Monocular Visual Odometry Estimation Using Inertially-Aided Convolutional Neural Networks, Josiah D. Watson
Theses and Dissertations
While Convolutional Neural Networks (CNNs) can estimate frame-to-frame (F2F) motion even with monocular images, additional inputs can improve Visual Odometry (VO) predictions. In this thesis, a FlowNetS-based [1] CNN architecture estimates VO using sequential images from the KITTI Odometry dataset [2]. For each of three output types (full six degrees of freedom (6-DoF), Cartesian translation, and transitional scale), a baseline network with only image pair input is compared with a nearly identical architecture that is also given an additional rotation estimate such as from an Inertial Navigation System (INS). The inertially-aided networks show an order of magnitude improvement over the …
Improving The Efficiency Of Dnn Hardware Accelerator By Replacing Digitalfeature Extractor With An Imprecise Neuromorphic Hardware, Majid Mohammadi Rad, Omid Sojodishijani
Improving The Efficiency Of Dnn Hardware Accelerator By Replacing Digitalfeature Extractor With An Imprecise Neuromorphic Hardware, Majid Mohammadi Rad, Omid Sojodishijani
Turkish Journal of Electrical Engineering and Computer Sciences
Mixed-signal in-memory computation can drastically improve the efficiency of the hardware implementing machine learning (ML) algorithms by (i) removing the need to fetch neural network parameters from internal or external memory and (ii) performing a large number of multiply-accumulate operations in parallel. However, this boost in efficiency comes with some disadvantages. Among them, the inability to precisely program nonvolatile memory devices (NVM) with neural network parameters and sensitivity to noise prevent the mixed-signal hardware to perform a precise and deterministic computation. Unfortunately, these hardware-specific errors can get magnified while propagating along with the layers of the deep neural network. In …
Detection Of Hand Osteoarthritis From Hand Radiographs Using Convolutionalneural Networks With Transfer Learning, Kemal Üreten, Hasan Erbay, Hadi̇ Hakan Maraş
Detection Of Hand Osteoarthritis From Hand Radiographs Using Convolutionalneural Networks With Transfer Learning, Kemal Üreten, Hasan Erbay, Hadi̇ Hakan Maraş
Turkish Journal of Electrical Engineering and Computer Sciences
Osteoarthritis is the most common type of arthritis. Hand osteoarthritis leads to specific structural changes in the joints, such as asymmetric joint space narrowing and osteophytes (bone spurs). Conventional radiography has traditionally been the primary method of visualizing these structural changes and diagnosing osteoarthritis. We aimed to develop a computerized method that is capable of determining the structural changes seen in radiography of the hand and to assist practitioners in interpreting radiographic changes and diagnosing the disease. In this retrospective study, transfer-learning-based convolutional neural networks were trained on a randomly selected dataset containing 332 radiography images of hands from an …
Efficient Turkish Tweet Classification System For Crisis Response, Saed Alqaraleh, Merve Işik
Efficient Turkish Tweet Classification System For Crisis Response, Saed Alqaraleh, Merve Işik
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a convolutional neural networks Turkish tweet classification system for crisis response. This system has the ability to classify the present information before or during any crisis. In addition, a preprocessing model was also implemented and integrated as a part of the developed system. This paper presents the first ever Turkish tweet dataset for crisis response, which can be widely used and improve similar studies. This dataset has been carefully preprocessed, annotated, and well organized. It is suitable to be used by all the well-known natural language processing tools. Extensive experimental work, using our produced Turkish tweet dataset …
A Sample Weight And Adaboost Cnn-Based Coarse To Fine Classification Of Fruit And Vegetables At A Supermarket Self-Checkout, Khurram Hameed, Douglas Chai, Alexander Rassau
A Sample Weight And Adaboost Cnn-Based Coarse To Fine Classification Of Fruit And Vegetables At A Supermarket Self-Checkout, Khurram Hameed, Douglas Chai, Alexander Rassau
Research outputs 2014 to 2021
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable lighting conditions and human factors arising from customer interactions with the system along with the challenges associated with the colour, texture, shape, and size of a fruit or vegetable. Considering this complex application, we have proposed a progressive coarse to fine classification technique to classify fruit and vegetables at supermarket checkouts. The image and weight of …
Flood Detection Using Multi-Modal And Multi-Temporal Images: A Comparative Study, Kazi Aminul Islam, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li
Flood Detection Using Multi-Modal And Multi-Temporal Images: A Comparative Study, Kazi Aminul Islam, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li
Electrical & Computer Engineering Faculty Publications
Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that …
Filter Design For Small Target Detection On Infrared Imagery Using Normalized-Cross-Correlation Layer, Hüseyi̇n Seçki̇n Demi̇r, Erdem Akagündüz
Filter Design For Small Target Detection On Infrared Imagery Using Normalized-Cross-Correlation Layer, Hüseyi̇n Seçki̇n Demi̇r, Erdem Akagündüz
Turkish Journal of Electrical Engineering and Computer Sciences
n this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similar to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation …
Context-Aware System For Glycemic Control In Diabetic Patients Using Neural Networks, Owais Bhat, Dawood A. Khan
Context-Aware System For Glycemic Control In Diabetic Patients Using Neural Networks, Owais Bhat, Dawood A. Khan
Turkish Journal of Electrical Engineering and Computer Sciences
Diabetic patients are quite hesitant in engaging in normal physiological activities due to difficulties associated with diabetes management. Over the last few decades, there have been advancements in the computational power of embedded systems and glucose sensing technologies. These advancements have attracted the attention of researchers around the globe developing automatic insulin delivery systems. In this paper, a method of closed-loop control of diabetes based on neural networks is proposed. These neural networks are used for making predictions based on the clinical data of a patient. A neural network feedback controller is also designed to provide a glycemic response by …
Applying Deep Learning Models To Structural Mri For Stage Prediction Of Alzheimer's Disease, Altuğ Yi̇ği̇t, Zerri̇n Işik
Applying Deep Learning Models To Structural Mri For Stage Prediction Of Alzheimer's Disease, Altuğ Yi̇ği̇t, Zerri̇n Işik
Turkish Journal of Electrical Engineering and Computer Sciences
Alzheimer's disease is a brain disease that causes impaired cognitive abilities in memory, concentration, planning, and speaking. Alzheimer's disease is defined as the most common cause of dementia and changes different parts of the brain. Neuroimaging, cerebrospinal fluid, and some protein abnormalities are commonly used as clinical diagnostic biomarkers. In this study, neuroimaging biomarkers were applied for the diagnosis of Alzheimer's disease and dementia as a noninvasive method. Structural magnetic resonance (MR) brain images were used as input of the predictive model. T1 weighted volumetric MR images were reduced to two-dimensional space by several preprocessing methods for three different projections. …
Quantifying Seagrass Distribution In Coastal Water With Deep Learning Models, Daniel Perez, Kazi Islam, Victoria Hill, Richard Zimmerman, Blake Schaeffer, Yuzhong Shen, Jiang Li
Quantifying Seagrass Distribution In Coastal Water With Deep Learning Models, Daniel Perez, Kazi Islam, Victoria Hill, Richard Zimmerman, Blake Schaeffer, Yuzhong Shen, Jiang Li
OES Faculty Publications
Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass …